{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read response functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "\n",
    "df_data=pd.read_pickle('FigS2B.pkl')\n",
    "\n",
    "DataRead=sio.loadmat('FigS2A.mat',squeeze_me=True)\n",
    "\n",
    "drf_dn_vsT=sio.loadmat('FigS2C.mat',squeeze_me=True)\n",
    "\n",
    "from types import SimpleNamespace \n",
    "data_mat = SimpleNamespace(**DataRead)\n",
    "\n",
    "judge=data_mat.judge\n",
    "edge_size=12\n",
    "mask=(df_data.nz_bg_rf[0].x>edge_size*1e-6) & (df_data.nz_bg_rf[0].x<(df_data.length[0]-edge_size*1e-6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "drf_dn_vsT_mat = SimpleNamespace(**drf_dn_vsT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_classic_edge=np.array([0.15,0.15,0.15])*1.5\n",
    "black_classic_face=np.array([0.4,0.4,0.4])*1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# plot density profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30.0, 120.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[2.,2.])\n",
    "ax=fig.add_axes([0.23,0.15,0.75,0.65])\n",
    "ax.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "\n",
    "plt.plot(df_data.Delta_rho_density[0].x*1e6,df_data.Delta_rho_density[0].y/1e18*judge,'--',color=colorstyle['blue_edge'])\n",
    "plt.plot(df_data.Delta_rho_density[0].x[mask]*1e6,df_data.Delta_rho_density[0].y[mask]/1e18*judge,'-',color=colorstyle['blue_edge'])\n",
    "\n",
    "plt.axvline(0,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "plt.axvline(df_data.length[0]*1e6,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "\n",
    "plt.xlabel('$z\\ (\\mathrm{\\mu m})$',fontsize=9,labelpad=1)\n",
    "plt.ylabel('$\\Delta n\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=1)\n",
    "plt.xlim([-30,120])\n",
    "# fig.savefig('dn.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30.0, 120.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[2.,2.])\n",
    "ax=fig.add_axes([0.23,0.15,0.75,0.65])\n",
    "ax.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "\n",
    "plt.plot(df_data.Delta_rho_rf[0].x*1e6,df_data.Delta_rho_rf[0].y/1e18,'--',color=colorstyle['blue_edge'])\n",
    "plt.plot(df_data.Delta_rho_rf[0].x[mask]*1e6,df_data.Delta_rho_rf[0].y[mask]/1e18,'-',color=colorstyle['blue_edge'])\n",
    "    \n",
    "plt.axvline(0,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "plt.axvline(df_data.length[0]*1e6,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "\n",
    "plt.xlabel('$z\\ (\\mathrm{\\mu m})$',fontsize=9,labelpad=1)\n",
    "plt.ylabel('$\\Delta n_\\mathrm{f}\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=1)\n",
    "plt.xlim([-30,120])\n",
    "# fig.savefig('dnrf.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=[2.,2.])\n",
    "ax1.set_position([0.23,0.15,0.65,0.65])\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_position([0.23,0.15,0.65,0.65])\n",
    "\n",
    "ax1.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "ax2.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "\n",
    "plt.sca(ax1)\n",
    "\n",
    "ax1.plot(df_data.Delta_rho_rf[0].x*1e6,df_data.Delta_rho_rf[0].y/1e18,'--',color=black_classic_edge)\n",
    "ax1.plot(df_data.Delta_rho_rf[0].x[mask]*1e6,df_data.Delta_rho_rf[0].y[mask]/1e18,'-',color=black_classic_edge)\n",
    "plt.axvline(0,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "plt.axvline(df_data.length[0]*1e6,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "\n",
    "plt.xlabel('$z\\ (\\mathrm{\\mu m})$',fontsize=9,labelpad=1)\n",
    "plt.ylabel('$\\Delta n_\\mathrm{f}\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=1,color=black_classic_edge)\n",
    "plt.xlim([-30,120])\n",
    "\n",
    "\n",
    "plt.sca(ax2)\n",
    "plt.plot(df_data.Delta_rho_density[0].x*1e6,df_data.Delta_rho_density[0].y/1e18*judge,'--',color=colorstyle['red_edge'])\n",
    "plt.plot(df_data.Delta_rho_density[0].x[mask]*1e6,df_data.Delta_rho_density[0].y[mask]/1e18*judge,'-',color=colorstyle['red_edge'])\n",
    "\n",
    "plt.axvline(0,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "plt.axvline(df_data.length[0]*1e6,color='k',alpha=0.5,linewidth=0.75,linestyle=':')\n",
    "plt.ylabel('$\\Delta n\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=2,color=colorstyle['red_edge'])\n",
    "\n",
    "fig.savefig('FigS2A.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linfun_ab(x,a,b): return a*x+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_classic=np.array([0.15,0.15,0.15])*1.5\n",
    "black_classic_face=np.array([0.4,0.4,0.4])*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[2.,2.])\n",
    "ax=fig.add_axes([0.23,0.15,0.65,0.65])\n",
    "ax.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "\n",
    "plt.plot(df_data.Delta_rho_density_rho0[0].y[mask]*judge*df_data.rho_0[0]/1e18,df_data.Delta_rho_rf_rho0[0].y[mask]*df_data.rho_0[0]/1e18,'o',\n",
    "         markersize=3,mec=black_classic_edge,mfc=black_classic_face,markeredgewidth=0.5)\n",
    "x_show=np.linspace(min(df_data.Delta_rho_density_rho0[0].y[mask]),max(df_data.Delta_rho_density_rho0[0].y[mask]))\n",
    "plt.plot(x_show*judge*df_data.rho_0[0]/1e18,linfun_ab(x_show,*df_data.dnrf_dn_slope[0])*df_data.rho_0[0]/1e18,color='k',alpha=0.5,linestyle='--',\n",
    "        linewidth=1.25)\n",
    "\n",
    "plt.ylabel('$\\Delta n_\\mathrm{f}\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=1)\n",
    "plt.xlabel('$\\Delta n\\ (\\mathrm{\\mu m}^{-3})$',fontsize=9,labelpad=1)\n",
    "\n",
    "plt.xlim([-0.1,0.1])\n",
    "plt.xticks([-0.1,0,0.1])\n",
    "# plt.xlim([-30,120])\n",
    "fig.savefig('FigS2B.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=[2,2])\n",
    "ax=fig.add_axes([0.23,0.15,0.65,0.65])\n",
    "ax.tick_params(axis='both',direction='in',labelsize=9,length=3)\n",
    "plt.errorbar(drf_dn_vsT_mat.TTilde,drf_dn_vsT_mat.dnrf_dn,yerr=drf_dn_vsT_mat.dnrf_dn_err,xerr=[drf_dn_vsT_mat.TTilde_err_left,drf_dn_vsT_mat.TTilde_err_right],marker='o',linestyle='None',\n",
    "             markersize=3,elinewidth=0.75,mew=0.75,mfc=black_classic_face,mec=black_classic_edge,\n",
    "             ecolor=black_classic_edge)\n",
    "\n",
    "plt.xlabel('$T/T_\\mathrm{F}$',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'$({\\partial{n_\\mathrm{f}}}/{\\partial{n}})_T$',fontsize=9,labelpad=1)\n",
    "\n",
    "\n",
    "fig.savefig('FigS2C.pdf',dpi=300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_out=pd.DataFrame({'z(um)':df_data.Delta_rho_density[0].x*1e6,'Delta n':df_data.Delta_rho_density[0].y/1e18*judge})\n",
    "# df_out.to_clipboard(ddindex=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_outB=pd.DataFrame({'Delta n':df_data.Delta_rho_density_rho0[0].y[mask]*judge*df_data.rho_0[0]/1e18,\n",
    "#                       'Delta nf':df_data.Delta_rho_rf_rho0[0].y[mask]*df_data.rho_0[0]/1e18})\n",
    "# df_outB.to_clipboard(index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_outB=pd.DataFrame({'TTilde':drf_dn_vsT_mat.TTilde,'TTilde_err_left':drf_dn_vsT_mat.TTilde_err_left,'TTilde_err_left':drf_dn_vsT_mat.TTilde_err_right,\n",
    "                      'dnf_dn':drf_dn_vsT_mat.dnrf_dn,'dnf_dn_err':drf_dn_vsT_mat.dnrf_dn_err})\n",
    "df_outB.to_clipboard(index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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